Under ideal conditions, the human visual system is only able to perceive from four to six bits of dynamic range (from 16 to 64 distinct gray levels) in a monochrome image. The actual number of bits perceived is further reduced in the presence of additive glare (background lights) which usually means that a darkened room is required for presentation of images with a large dynamic range. The perceived dynamic range of an image may be increased by reducing the amplitude of lower spatial frequencies (by homomorphic filtering), but this dynamic range reduction is detrimental to effective diagnosis of medical images, particularly ultrasound images, which makes use of the presence of shadowing and/or bright-up.
It is well known that pseudocolor enhances human perception of gray scales and enables an observer to quantify a single parameter image. It has also been shown that color can allow the eye-brain combination to form useful correlations on multidimensional image data, if it can be used in a pleasing manner. However, many medical radiologists do not like pseudocolor images.
It is known that separate use of red, green and blue to image three independent variables leads to confusing images. A more natural presentation is used in commercial color television and in map making where a primary image is shown as a high (spatial) resolution intensity image and secondary parameters are visualized by color tinting (which typically has less spatial resolution than the primary image). Thus, the hue (color) and the saturation (purity of color) can be used to present two independent low-resolution variables which are superimposed on a high resolution intensity image.
FIG. 1 schematically illustrates a two-dimensional chromaticity space of hue and saturation in polar coordinates.
In the figure, S=0 is white (zero color saturation) and the circle S=1 represents pure monochromatic colors (fully saturated). In this model, locations near S=0 are pastel colors.
In the prior art, three parameters of position in an image (such as a(x,y), f(x,y) and g(x,y)) were respectively assigned to Intensity, Hue and Saturation as, for example:
______________________________________ Intensity: I(x,y) = K.sub.i a(x,y) (1) Hue: H(x,y) = K.sub.h f(x,y) (2) Saturation: S(x,y) = K.sub.s g(x,y) (3) ______________________________________
where K.sub.i, K.sub.h, and K.sub.s are constants.
The simplest use of these relations is to set S=0 (which implies that the hue is irrelevent). Black and white intensity images are thus produced from the function a(x,y).
In the usual prior art pseudocolor display, the parameter, I(x,y) is a constant, S is unity (fully saturated color), and the parameter f(x,y) is imaged with hue as the only variable. Many people find such images distasteful.
In a more acceptable prior art method, "color tinting" of a gray scale image is accomplished using all three equations. If two parameters are of interest, it is common to set S equal to a constant and to use I(x,y) and H(x,y) as the parametric variables. Color tinting of a black and white image usually conveys low resolution information as an overlay through which the observer can see an image of intensity information a(x,y) in the same manner as a color tinted black and white photograph. If S is chosen as unity, this scheme is still unsatisfying to many observers. More pleasing images are formed if S is set to a small value which presents all colors as unsaturated pastels.
Many algorithms for compressing the dynamic range of a one-dimensional image exist in the prior art. For example, homomorphic filtering which can emphasize local high frequency variations in an image while suppressing low spatial frequency variations is described in Oppenheim, Schaffer and Stockham, "Nonlinear Filtering of Multiplied and Convolved Signals", Proceedings of the IEEE, Vol. 56, pgs. 1264-1291 (1968) and in "Digital Image Restoration", Andrews and Hunt, Prentice-Hall Inc., 1977, which are incorporated herein, by reference, as background information. In one such technique, the average value of pixels in the local two-dimensional region surrounding each pixel is computed and is divided into the value of the individual pixel. The quotient represents the local, high frequency variation and is used to intensity modulate the display of the pixel. This results in a compression, similar to AGC compression of audio signals. However, these techniques generally "throw-away" the low spatial frequency, background information from the image.